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Predictive Analytics in Retail Banking

What is predictive analytics in retail banking

Predictive analytics in retail banking refers to the use of computer models that rely on artificial intelligence and data mining to analyze large amounts of information and to predict future customer behavior.

What are the business benefits of predictive analytics in retail banking?

Predictive analytics can help banks by providing deep insights into customer needs; launch innovative products and services; deliver personalized and stellar experiences; lead to new business models; and transform using new processes and technology. Related benefits of predictive analytics include:

  • Deep insights into customer needs—Better customer insights enable lenders to more effectively target their customers with relevant and thoughtful services at the appropriate moment.
  • Launch innovative products and services—More effective redesign of products and services based on customer research, segmentation and analysis, enhanced portfolio strategies and pricing.
  • Providing personalized and stellar experiences—Enhanced convenience and customer engagement by providing a consistent, convenient and synchronous experience through all interaction channels and across all devices.
  • Business model disruption and innovation—Faster adoption of new business models, digital products, pricing and packaging to meet customer needs.
  • Processes/ technology transformation—Transformation of non-customer facing processes for efficiency and effectiveness, and launch of new products and services providing superior customer experiences.

Predictive Analytics in Retail Banking Featured Content
Building an Effective & Extensible Data & Analytics Operating Model

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